The key to meaningful analysis is the ability to choose the right methods that provide the greatest predictive power. Explore how data clustering, such as K-Means, hierarchical, and DBSCAN, is used to combine similar subsets of data.
Using Clustering Techniques
- start the course
- recognize characteristics of clustering
- identify the different types of clustering
- calculate proximity
- list key features of K-Means Clustering
- recognize key steps for reducing the sum of squared errors in K-Means Clustering
- recognize key steps for the termination of K-Means Clustering iterations
- evaluate K-Means Clustering
Hierarchical Clustering and DBSCAN
- list key features of Hierarchical Clustering and DBSCAN
- recognize key steps in DBSCAN
- identify key attributes for performing DBSCAN
If you would like to provide feedback for this course, please e-mail the NICCS SO at NICCS@hq.dhs.gov.